The relationship between active travel to school and health

REVIEW Open Access

The relationship between active travel to schooland health-related fitness in children andadolescents: a systematic reviewDavid R Lubans1*, Colin A Boreham3, Paul Kelly2, Charlie E Foster2

Abstract

Background: Active travel to school (ATS) has been identified as an important source of physical activity for youth.However, the relationship between ATS and health-related fitness (HRF) among youth remains unclear.

Methods: A systematic search of seven electronic databases (EMBASE, OVID MEDLINE, PsycINFO, PubMed, Scopus,SPORTDiscus and TRIS on line) was conducted in December 2009 and studies published since 1980 wereconsidered for inclusion.

Results: Twenty seven articles were identified that explored the relationship between ATS and the followingaspects of HRF: weight status/body composition, cardiorespiratory fitness, muscular fitness and flexibility. Forty-eight percent of the studies that examined the relationship between ATS and weight status/body compositionreported significant associations, this increased to 55% once poor quality studies were removed. Furthermore, thefindings from five studies, including one longitudinal study, indicate that ATS is positively associated withcardiorespiratory fitness in youth. However, the evidence for the relationships between ATS and muscular fitness orflexibility is equivocal and limited by low study numbers.

Conclusions: There is some evidence to suggest that ATS is associated with a healthier body composition andlevel of cardiorespiratory fitness among youth. Strategies to increase ATS are warranted and should be included inwhole-of-school approaches to the promotion of physical activity.

BackgroundHigher levels of physical activity are associated withsuperior physical, social and psychological health inyoung people. Physical activity is inversely related tooverweight and obesity in youth [1,2] and poor bodycomposition in childhood is associated with an increasedrisk of coronary heart disease in adulthood [3]. Whilethere has been debate about whether or not physicalactivity levels have declined in the last 30 years [4], evi-dence suggests that there has been a decline in activetravel to school (ATS) among children and adolescentsin many countries [5-7]. For example, data from the USNational Personal Transportation Survey revealed thatthe number of students who walked or cycled to schooldecreased from 41% in 1969 to only 13% in 2001 [6].

ATS includes various modes of travel such as walking,cycling, and skateboarding, and has been identified as animportant source of physical activity for young people [8].Previous reviews [9-11] have examined the relationshipbetween ATS, body composition and physical activity andreported strong evidence for a positive associationbetween ATS and overall physical activity level, but littleevidence linking ATS to leaner body composition. How-ever, these reviews did not stratify their results or conclu-sions by study quality or risk of bias [12]. While previousreviews included studies that examined the relationshipbetween ATS and cardiorespiratory fitness, the authorswere unable to determine the association due to limitedevidence. Since the publication of these reviews there hasbeen an increase in the number of studies exploring thisrelationship.It has been suggested that much of the activity com-

pleted by children and adolescents is of insufficientvolume and intensity to improve health-related fitness

* Correspondence: [email protected] of Education, University of Newcastle, Callaghan Campus, AustraliaFull list of author information is available at the end of the article

Lubans et al. International Journal of Behavioral Nutrition and Physical Activity 2011, 8:5http://www.ijbnpa.org/content/8/1/5

© 2011 Lubans et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

mailto:[email protected]

http://creativecommons.org/licenses/by/2.0

(i.e., body composition, cardiorespiratory fitness, muscu-lar fitness and flexibility) [13]. While the exact volumeand intensity of activity required to improve fitness inyoung people is not clear [14], it is plausible to suggestthat habitual ATS has the potential to improve health-related fitness (HRF) among youth. ATS provides a reg-ular opportunity for young people to accumulate physi-cal activity, but evidence to support or refute the healthbenefits of active transportation is limited due to thefocus on assessing time spent in vigorous activity [8].Furthermore, few studies have explored the effects ofATS on HRF among young people in longitudinal stu-dies [15]. The aim of this review, therefore, was to sys-tematically examine the potential health benefitsassociated with ATS among children and adolescents.The secondary aim was to explore the quality of studiesthat have examined the relationship between ATS andHRF and to determine whether study quality may haveconfounded these relationships.

MethodsIdentification of studiesThe Quality of Reporting of Meta-analyses statement(QUOROM) [16] provided the structure for this review.A systematic search of seven electronic databases(EMBASE, OVID MEDLINE, PsycINFO, PubMed, Sco-pus, SPORTDiscus and TRIS Online) was conducted inDecember 2009. Studies published from 1980 up to andincluding the search date were considered for inclusion.Individualized search strategies for the different databasesincluded combinations of the following key words: (child*OR adolescent OR youth OR “young people”) AND(walking OR “active travel” OR “active transport*” ORcycling OR riding OR “travel mode” OR trip) AND("health-related fitness” OR “body composition” OR “obe-sity” OR “weight status” OR “physical fitness” OR fitnessOR “cardiovascular fitness” OR “cardiorespiratory fitness”OR “aerobic fitness”). Only articles published or acceptedfor publication in refereed journals were considered forthe review. Conference proceedings and abstracts, there-fore, were not included. In the first stage of the literaturesearch, titles and abstracts of identified articles werechecked for relevance. In the second stage, full-text arti-cles were retrieved and considered for inclusion. In thefinal stage, the reference lists of retrieved full-text articleswere searched and additional articles known to theauthors were assessed for possible inclusion.

Criteria for inclusion/exclusionIn the first stage of the literature search, two of the fourauthors (DRL and PK) independently assessed the stu-dies for inclusion. Studies were considered to be eligiblefor inclusion according to the following criteria: (i) par-ticipants were children or adolescents aged 5 to

18 years, (ii) study reported active school transportationin children and/or adolescents, (iii) study assessed atleast one component of health-related fitness (i.e. cardi-orespiratory fitness, muscular fitness, body compositionor flexibility), (iv) study included quantitative analysis ofthe relationship between ATS and at least one compo-nent of health-related fitness, (v) study published inEnglish.

Criteria for assessment of study qualityTwo authors (DRL and PK) independently assessed thequality of the studies that met the inclusion criteria(Table 1). The criteria for assessing the quality of thestudies were adapted from the Strengthening theReporting of Observational Studies in Epidemiology(STROBE) statement [17]. Items that were considered tobe the most important in relation to study bias wereincluded in the concise checklist. A formal quality scorefor each study was completed for a 6-item list by assign-ing a value of 0 (absent or inadequately described) or 1(explicitly described and present) to each of the follow-ing questions listed: (i) Did the study describe the parti-cipant eligibility criteria? (ii) Were the study schools/participants randomly selected (or representative of thestudy population)? (iii) Did the study report the sourcesand details of ATS assessment and did the instrumentshave acceptable reliability for the specific age group?(iv) Did the study report the sources and details of HRFassessment and did all the methods have acceptablereliability? (v) Did the study report a power calculationand was the study adequately powered to detecthypothesized relationships? (vi) Did the study report thenumbers of individuals who completed each of the dif-ferent measures and did participants complete at least80% of measures? Differences were resolved by a thirdreviewer (CEF).

Categorization of variables and level of evidenceThe level of association between ATS and HRF wasdetermined using the coding system first described bySallis et al. [18] (Table 2). The relationship between ATSand HRF components was determined by examining thepercentage of studies that reported a statistically signifi-cant relationship (i.e. between ATS and the HRF compo-nent). If only 0-33% of the included studies reporteda statistically significant relationship between ATS and agiven HRF component, the result was categorized as noassociation (0). If 34-59% of the studies reported statisti-cally significant relationships between ATS and the HRFcomponent, the result was categorized as indeterminate(?). If 60-100% of studies reported a positive relationshipbetween ATS and the HRF component, the result wascoded as a positive association (+). If 60-100% of studiesreported a negative relationship between ATS and the


of 12

Table 1 ATS study quality checklist with quality scores assigned

Studies (i) Did thestudy

describethe

participanteligibilitycriteria?

(ii) Were thestudy schools/participantsrandomlyselected (or

representativeof the studypopulation)?

(iii) Did the studyreport the sourcesand details of ATSmeasurement anddid the methodshave acceptablereliability for the

specific age group?

(iv) Did the studyreport the sourcesand details of HRFassessmenta anddid the all of themethods haveacceptable

reliability for thespecific age group?

(v) Did the studyreport a powercalculation andwas the studyadequatelypowered to

detecthypothesizedrelationships?

(vi) Did the studyreport the numbersof individuals whocompleted each of

the differentmeasuresb and did

participantscomplete at least80% of measures?

Qualityscoretotal/6

Evenson et al[19]

1 1 0 0 0 1 3

Tudor-Lockeet al [8]

1 1 0 1 0 0 3

Metcalf et al[26]

1 0 0 1 1 0 3

DeBourdeauhuijet al [30]

0 1 1 0 0 1 3

Fulton et al[21]

0 1 0 0 0 1 2

Gordon-Larsen et al[20]

1 1 0 1 0 1 4

Heelan et al[43]

1 0 1 1 1 0 4

Klein-Platatet al [31]

1 1 1 1 0 0 4

Sirard et al[22]

1 1 0 1 0 0 3

Cooper et al[32]

1 1 0 1 0 1 4

Mota et al[35]

1 0 0 1 0 1 3

Rosenberget al [23]

1 0 0 1 0 1 3

Timperio et al[36]

1 1 1 1 0 1 5

Ford et al[27]

1 0 0 1 0 0 2

Li et al [38] 1 1 0 1 0 0 3

Mota et al[34]

1 0 0 1 0 1 3

Ortega et al[39]

1 1 0 1 0 0 3

Saksvig et al[24]

1 1 1 1 0 0 4

Cooper et al[33]

1 1 0 1 0 1 4

Landsberget al [40]

1 0 1 1 0 1 4

Robertson-Wilson et al[41]

1 1 1 1 0 1 5

Silva andLopez [42]

1 1 1 1 1 1 6

Yeung et al[37]

1 0 0 0 0 1 2

Andersenet al [15]

1 1 0 1 0 0 3

Baig et al [28] 1 0 1 1 1 1 5


of 12

HRF component, the result was coded as a negative asso-ciation (-). The methods of Sallis et al. [18] were modifiedto address the issue of study quality, and additional cod-ing was conducted based on studies identified as highquality. If 60-100% of high quality studies (≥ 4) found arelationship between ATS and a given HRF component,the result was coded as having strong evidence for a posi-tive association (++) or negative association (–).

ResultsOverview of studiesThe flow of studies through the search process isreported in Figure 1. While the majority of studies(Table 3) were conducted in the US [19-25], the rela-tionship between ATS and HRF was explored in afurther 12 different countries, including the Philippines[8], England [26-29], Holland [30], France [31], Den-mark [15,32,33], Portugal [34,35], Australia [36,37],China [38], Spain [39], Germany [40], Canada [41], andBrazil [42]. The study sample sizes ranged from 107 [37]to 21,345 [41]. While most of the studies involved cross-sectional study designs, Cooper and colleagues [33]examined longitudinal changes in transport to schoolover a 6-year study period. Similarly, Rosenberg et al[23] explored the relationship between school travel andweight status over a 2-year period. The prevalence of

ATS among study participants ranged from 3 and 8% inboys and girls [22], to 72 and 79% in boys and girls [15].

Overview of study qualityThere was 90% agreement between reviewers (DRL andPK) on the study assessment criteria and full consensuswas achieved with the help of a third reviewer (CEF). Ele-ven studies scored ≥ 4 on the study quality assessment[20,24,28,31-33,36,40-43] (Table 1). The majority of studiesused samples of schools/participants who were randomlyselected and/or representative of the study population.Most of the studies described the participant eligibility cri-teria, although participant eligibility (e.g. were participantsselected from all grades at participating schools?) wasunclear in three studies [21,29,30]. The majority of studiesreported standardized procedures for the assessment ofheight, weight and cardiorespiratory fitness. Two studiesreported parental proxy reports for their children’s heightand weight and did not provide reliability/validity data[21,37] and a further two studies used self-reported heightand weight to calculate BMI [19,30]. While the majority ofstudies involved large sample sizes and were adequatelypowered to detect hypothesized relationships, only two stu-dies reported power calculations relevant to their studyaims [42,43]. Fifteen studies reported the number of indivi-duals who completed each of the different measures.

Table 1 ATS study quality checklist with quality scores assigned (Continued)

Madsen et al[25]

1 0 0 1 0 1 3

Voss et al[29]

0 1 0 1 0 1 3

Note. 1 = Yes and 0 = No; ATS = active travel to school.aThe measurement of height and weight using standardized procedures to calculate BMI satisfied this criteria.bStudies were required to report the number of participants who completed ATS questionnaire and HRF measures separately.

Table 2 Summary of studies examining the relationship between ATS and HRF in youth

Benefits Associated with ATS Not associated with ATS Summary codinga

Reference no. Assoc.(-/+)b

Reference no. n/N (%)c Assoc.(-/+/?)d

Weight status/bodycomposition

[19,8]e,[20,30,43,31,23]e,[38,39]f,[40,42,25]

- [21,26,22,35,36,27,34,24,41,37,15,28],[29]

12/25(48%)

?

Cardiorespiratory fitness [15]g,[32]g,[33]g,[29] + [25] 4/5 (80%) +

Muscular fitness [15] + 1/1 (100%) ?

Flexibility [15]h + 1/1(100%) ?

Note. ATS = active travel to school; A positive or negative association was noted if at least one component of ATS was associated with the HRF component.aSummary code provides an overall summary of the findings for each relationship.bAssociation shows the direction of the individual association.cN = number of studies that examined and reported possible associations between ATS and HRF component, n = number of studies that support therelationship.dAssociation for overall findings for each relationship for high quality studies. If 60-100% of high quality studies (≥ 4) found a relationship between ATS and HRFcomponent, the result was coded as having strong evidence for a positive association (++) or negative association (–). If < 4 studies available the relationshipwas coded as indeterminate (?).eRelationship for boys only.fRelationship for girls only.gRelationship between CRF and cycling.hCyclists had better flexibility than walkers and those who used PTS.


of 12

Overview of findingsTwenty three studies examined the relationship betweenATS and weight status/body composition (Table 2).ATS was associated with more beneficial weight status(or lower body fat) in 11 (48%) of these studies. How-ever, when lower quality studies (< 4) were excludedfrom the summary, five of the nine remaining studiesfound ATS to be beneficially associated with weight sta-tus. Five studies examined the association between ATSand CRF [15,25,29,32,33]. Four of these studies[15,29,32,33] found a positive association, while the fifthstudy [25] found a non-significant (p < .07) trendbetween ATS and CRF. More specifically, three of fourstudies involving cycling reported positive associationswith CRF, and a 6-year longitudinal study [33] demon-strated that change in travel mode to school from non-cycling to cycling was a significant predictor of CRF atfollow-up. Further evidence supporting the benefits ofcycling was provided by Andersen and colleagues [15],who found that muscular fitness and flexibility were sig-nificantly better among Danish cyclists compared towalkers and those who used passive transportation.

DiscussionThe aim of this review was to systematically examinethe potential health benefits associated with ATS amongchildren and adolescents. Of the 27 studies that wereincluded in the review, 25 examined the relationship

between ATS and weight status/body composition.While only 48% of these studies reported significantinverse associations between ATS and weight status/body fatness, this increased to 55% once poor qualitystudies were removed. Based on the findings from fivestudies, including one longitudinal study, it appears thatATS may be associated with superior cardiorespiratoryfitness in youth. It was not possible to draw any conclu-sions about the associations between ATS, muscular fit-ness and flexibility due to the small number of studiesexploring these relationships.Despite methodological differences, these results are

similar to those of previous reviews [9,10]. Two previousATS and health reviews reached the same conclusionabout a lack of association between body weight andATS despite not assessing the quality of their includedstudies. The decision to use an assessment of studyquality or risk of bias was both pragmatic and justified.The application of this metric allowed for the removalthe studies with the greatest risk of bias. Faulkner andcolleagues’ review [10] reported just over half the studiesincluded in this review (n = 10), that had examinedassociations between ATS and body weight. One expla-nation for this difference lies in the recent increase inATS-related publication since their review’s search date(06/2008). Their review not only focused on the rela-tionship between ATS and overall physical activity, butalso adopted inclusion criteria for studies for the assess-ment of physical activity using an objective measure(pedometer or accelerometer). Despite these differences,the findings of both reviews were consistent, concludingthat there is little evidence to suggest an associationbetween body weight and ATS.Previous reviews that examined the relationship

between ATS and cardiorespiratory fitness were unableto draw conclusions due to the small number of studies.However, evidence from a number of recent welldesigned studies suggests a positive association betweenATS and cardiorespiratory fitness. One partial explana-tion for these positive associations may be the use ofcycle ergometers to measure fitness in three of the fivestudies, which may have favored those children whocycled to school [15,32,33]. However, the two other stu-dies that used running-based fitness assessments alsoobserved differences in fitness between ATS and nonATS groups, with one significant [29] and one border-line nonsignificant [25] result. Furthermore, a recentstudy, not included in the current review, found thatCRF levels were higher among young Swedish and Esto-nians adolescents who cycled to school compared tothose who walked or used passive transportation meth-ods [44]. There appears, therefore, to be some evidencesupporting the observation that fitness is associated withATS, but future studies must account for loss to follow-

STAGE 1 Total hits from database searches

= 2226

Studies excluded based on title and review of abstract = 2098

STAGE 2 Full-text articles retrieved

= 128

Studies excluded based on full-text review = 113

31 wrong study aim 13 review paper 21 included participants >18yrs 19 no measure of ATS 29 no measure of HRF

STAGE 3 15 references lists searched

12 additional studies found in reference list search

Articles included in review =27

Figure 1 Flow diagram of study selection. Note. ATS = activetravel to school, HRF = health-related fitness.


of 12

Table 3 Summary of included studies

Study Sample Studydesign

ATS classification HRF component(s) and method ofassessment

Analyses Results Prevalence of ATS

Evenson et al[19]

4448adolescentsGrades 6-

12UnitedStates

Cross-sectional

Walking or riding to/from school atleast once/week

BC- Self report BMI z-score (>85th

percentile considered overweight)Logistic

regressionMiddle school students above the85th percentile were less likely to useATSBMI categories not associated withATS in high school students.

6th grade- 12.3%7th grade- 7.5%8th grade- 8.2%9th grade- 6.0%10th grade- 5.7%11th grade- 4.0%12th grade- 3.0%

Tudor-Lockeet al [8]

1518adolescents14-16 yearsPhilippines

Cross-sectional

Usual travel to/from school (ATS,combination of ATS and PTS or PTS

only)

BC- BMI ANOVA BMI not associated with ATS in girls.Boys using ATS only had significantlylower BMI values than boys usingPTS only.

Boys- 47% (323/691)Girls- 37% (303/827)

Metcalf et al[26]

275children5 yearsEngland

Cross-sectional

Usual travel to/from school (ATS orPTS)

BC- BMI and sum of 5 skinfolds ANOVA No relationship between ATS andBMI or skinfolds.

Boys- 63% (97/154)Girls- 73% (88/121)

DeBourdeauhuijet al [30]

6078childrenand

adolescents11-17 yearsBelgium

Cross-sectional

Usual travel to/from school (ATS orPTS)

BC- Self-report BMI z-score (>85th

percentile considered overweight)Independentsamples t-test

Overweight youth less likely to useATS.

Not available

Fulton et al[21]

1395childrenand

adolescentsGrades 4

to 6, 7 to 9and 10 to

12UnitedStates

Cross-sectional

Usual travel to/from school or work(ATS or PTS)

BC- Proxy and self-report BMI Logisticregression

No association between BMI andATS.

Boys- 17% (121/727)Girls- 11% (74/668)

Gordon-Larsen et al[20]

10771adolescentsGrades 7to 12UnitedStates

Cross-sectional

Usual travel to/from school or work(ATS or PTS)

BC- BMI Independentsamples t-test

Rates of ATS were higher amongnon-overweight adolescents.

Heelan et al[43]

320children10.2 yearsUnitedStates

Cross-sectional

How they travelled to/from schoolin the past 24 hours (ATS or PTS)

and amount of time taken

BC- BMI, average of 3 skinfolds Multipleregression

Significant association between BMIand ATS in overweight children. Noassociation between skinfolds andATS.

Boys & girls- 33.3%(107/320)

Lubanset

al.InternationalJournalofBehavioralN

utritionand

PhysicalActivity

2011,8:5http://w

ww.ijbnpa.org/content/8/1/5

Page6of

12

Table 3 Summary of included studies (Continued)

Klein-Platat etal [31]

2714adolescents12 yearsFrance

Cross-sectional

Walking or riding to/from school-none, 0-20 min/day and > 20 min/

day

BC- BMI z-score (>90th percentileconsidered overweight) and WC

ANCOVA ATS associated with weight statusand WC.

Boys- 40% (543/1357)Girls- 37% (503/1357)

Sirard et al[22]

219children10.3 yearsUnitedStates

Cross-sectional

Walking or riding to/from school-regular active commuters (>5times/week), irregular active

commuters (1-4 times/week) orpassive commuters (0 times/week)

BC- BMI z-score (>85th percentileconsidered overweight)

ANOVA No association between weightstatus and ATS.

Boys- 3% (3/96)Girls- 8% (8/123)

Cooper et al[32]

529childrenand 390

adolescents9.7 and15.5 yearsDenmark

Cross-sectional

Usual travel to/from school [passive(car, motorcycle, train, bus), bicycleor walk] and duration of journey

CRF- Progressive cycle ergometer ANOVA Children and adolescents who cycledto school had higher CRF than thosewho used PTS.No relationship between walking toschool and CRF.

Boys (child)-66% (168/254)Girls (child)- 62%(172/276)Boys (adol)-87.5%(169/193)Girls (adol)-85% (167/196)

Mota et al[35]

450adolescents14.6 yearsPortugal

Cross-sectional

Usual travel to/from school (ATS orPTS) and duration of journey


Bivariatecorrelation

No association between weightstatus and ATS.

Boys & girls- 23.1%(105/450)

Rosenberg etal [23]

1083childrenGrades 4and 5UnitedStates

Longitudinal ATS- 2 or more days/week of ATSat each of the 4 assessment

periods.

BC- BMI z-score (>85th percentileconsidered overweight), average of

3 skinfolds

ANOVA Boys who used ATS at baseline hadsignificantly lower BMI and skinfoldsthan passive commuters.No association between ATS andweight status in girls.No association between changes inBMI and skinfolds and ATS over 2-year period.

Boys- 36% (116/320)Girls- 29% (79/274)

Timperio et al[36]

912children

5 to 6 and10 to 12years

Australia

Cross-sectional

ATS at least once per week BC- BMI z-scores (IOTF classification) Logisticregression

No association between ATS andweight status.

Boys- 9% (29/316)Girls- 4% (13/361)

Ford et al[27]

239children5 to 11years

England

Cross-sectional

PST or ATS (walk) for > 10 mins,more than 3 times/week for at least

15 weeks

BC- Air displacementplethysmorgaphy (BodPed Self-Test)

Mann-WhitneyU test

No significant difference in body fatbetween ATS a

Boys- 47% (59/125)Girls- 43% (49/114)

Lubanset


utritionand

PhysicalActivity

2011,8:5http://w


Page7of

12


Li et al [38] 6826childrenand

adolescents7 to 17yearsChina

Cross-sectional

Usual travel to/from school- walkingor riding classified as ATS


Cox regressionanalysis

controlling forparental

overweightand SES

Overweight youth less likely to useATS.

Boys & girls- 93.6%(6386/6826)

Mota et al[34]

705adolescent

girls14.7 yearsPortugal

Cross-sectional

Usual travel to/from school- walkingor riding classified as ATS

BC- BMI Chi-square andbivariatecorrelation

No association between BMI andATS.

Boys & girls- 52.6%(371/705)

Ortega et al[39]

2859adolescents13 to 18.5

yearsSpain

Cross-sectional

ATS classified as riding/walking to/from school > 15 min/day

BC- BMI z-scores, WC adjusted forheight

ANCOVA Weights status not associated withATS in boys or girls. Significantassociation between ATS and WC ingirls.

Boys- 9.6% (130/1357)Girls- 13% (189/1502)

Saksvig et al[24]

1721adolescent

girls12.0 yearsUnitedStates

Cross-sectional

Travel by walking on 1 or moreweekdays before/after school.

BC- BMI Linear mixedmodels

No difference in BMI among thosewho walked to school and thosewho did not.

Before school - 13.6%(232/1701)After school 17.7%(301/1701)

Cooper et al[33]

384children9.7 yearsDenmark

Longitudinal Usual travel to/from school (cycle,walk or PTS).

CRF- Progressive cycle ergometer ANOVA CRF was significantly higher amongchildren and adolescents who cycledto school at one or both timeperiods compared to those whoused other forms of transport.

Boys- 66% (110/170)Girls- 65% (137/214)

Landsberg etal [40]

626adolescents14 yearsGermany

Cross-sectional

Usual travel to/from school (ATS orPTS) and duration of journey.

BC- BMI z-score (>85th percentileconsidered overweight), Sum of 4skinfolds (triceps, biceps, suprailiacal

and subscapular), bioelectricalimpedance (fat mass) and WC.

General linearmodels

ATS associated with lower fat massand skinfolds. ATS associated withBMI or WC.

Boys- 63% (206/328)Girls- 50% (163/298)

Robertson-Wilson et al[41]

21345adolescentsGrades 9to 12Canada

Cross-sectional

Usual travel to/from school (ATS orPTS).


Binary logisticregression


Boys- 44% (4699/10747)Girls- 41% (4378/10598)

Silva andLopez [42]

1570children7 to 12yearsBrazil

Cross-sectional

Usual travel to/from school (ATS orPTS) and duration of journey.

BC- BMI z-scores (IOTF classification)and skinfolds (tri-cipital)

Regression ATS was associated with a lowerprevalence of excess weight andbody fat.

Boys- 15% (117/785)Girls- 18% (131/742)

Lubanset


utritionand

PhysicalActivity

2011,8:5http://w


Page8of

12


Yeung et al[37]

107children4 to 12years

Australia

Cross-sectional

ATS at least once/week. BC- Parental proxy BMI Mann-WhitneyU test

No association between BMI andmode of transportation to school.

Boys- 40% (59/149)Girls- 27% (46/169)

Andersen etal [15]

1249adolescents15 to 19years

Denmark

Cross-sectional

Usual travel to/from school (cycle,walk or PTS).

BC- BMI CRF- Progressive cycleergometer

MF- sit-ups, static back strength, armflexion dynamic testFL- sit and reach

ANOVA Cyclists had higher CRF, MF and FLthan both walkers and those whouse PTS.No relationship between BMI andtransportation mode.

Boys- 72% (391/545)Girls- 79% (559/704)

Baig et al [28] 673adolescents12.6 yearsEngland

Cross-sectional

Usual travel to/from school (cycle,walk, public transport or bus) andduration of journey calculated.

BC- BMI z-scores (IOTF classification) Binary logisticregression


Girls 3.6 times morelikely to walk toschool than boys

Madsen et al[25]

5357adolescentsGrades 7and 9UnitedStates

Cross-sectional

Travel to (day of survey) and from(day before survey) school.

BC- BMI z-scores (CDC >85th

percentile considered overweight)CRF- 1 mile run test

Linearregression

ATS was inversely associated withweight statusA non-significant (p = 0.07) trendbetween ATS and CRF was found.

To school - 29%(1554/5357)From school - 46%(2464/5357)

Voss et al[29]

6085childrenand

adolescents10 to 15.9

yearsEngland

Cross-sectional

Usual travel to/from school (cycle,walk, public transport or bus) andduration of journey calculated.

BC- BMI z-scores (IOTF classification)CRF- 20 m shuttle run test

ANOVA No association between travel modeand BMI. ATS associated withimproved CRF.

Boys- 59% (1845/3135)Girls- 57% (1587/2792)

ATS = Active travel to school; BC = Body composition; BMI = Body mass index; CDC = Centers for Disease Control & Prevention; CRF = Cardiorespiratory fitness; FL = Flexibility HRF = Health-related fitness;IOTF = International Obesity Task Force; MF = Muscular fitness; NR = Not reported; PTS = Passive travel to school; WC = Waist circumference.

Lubanset


utritionand

PhysicalActivity

2011,8:5http://w


Page9of

12

up and validated assessments of ATS to improve confi-dence in these observations.Similar limitations to the overall quality of studies

were observed, particularly in the definition and mea-surement of ATS. There were a range of definitions forclassifying a participant as an active traveler. These defi-nitions used different categories for frequency, durationand type of activity that counted as ATS. For example,studies often used questions about “usual mode of travelto and from school”, and only 11 of the 27 studiesreported an acceptable reliability of their methodologyfor ATS assessment in the sample population. Thus,standardized definition and measurement should beaddressed in future studies, as active travel is a collec-tion of behaviors that vary by purpose and duration[45], e.g. journey to and from school. The potential formisclassification of ATS was also reflected in the actualmeasurement of ATS mode, with no study reporting avalidated measure of ATS by travel mode. With theadvent of both portable global positioning systemdevices and digital image capture systems, the opportu-nity to improve this potential source of bias must beaddressed. These new systems, which allow the confir-mation of journey mode, duration and distance, are cur-rently being evaluated in a number of internationalstudies [46,47]. Such systems would also address theissue of possible confounding or mediation of ATSbehavior and health outcome by the built environmentcharacteristics of study areas [48].The majority of studies did not examine associations

by population sub- groups, e.g. gender or age. Indeed,study populations were treated as homogeneous, beingusually dichotomized into ATS v non-ATS groups. Suchan approach is limited as it ignores the broader social,environmental and personal level correlates of behavior.Indeed, Panter and colleagues [48] highlighted theimportance of facilities to assist active travel and urbandesign in the neighborhood, as well as shorter distancesand road safety in-route in relation to active travel. Arecent review of qualitative studies of children’s experi-ences of active travel not only supported these observa-tions, but also highlighted the importance of theconstraining influence of parents’ restrictions on inde-pendent movement, as well as children’s own fears oftraffic [49]. All these factors clearly have a major impacton children’s ability to undertake ATS and may providean explanation why some studies fail to report consis-tent associations [9,10]. Only two studies reportedpower calculations in their methods sections. While themajority of the studies included large samples and werelikely to have adequate power to detect hypothesizedrelationships, the reporting of power calculations shouldbe considered by others in the future.

Body composition/weight status was measured in avariety of ways, with most studies employing BMIz-scores or percentiles to classify youth as healthy, over-weight or obese. Two studies [21,37] used parental proxyreports of their children’s height and weight and bothstudies found no association between BMI and methodof transportation to school. Alternatively, two studies[19,30] used students’ self-reported BMI and found sig-nificant associations between ATS and weight status.More consistency in using a more objective assessmentof this exposure variable in future studies would help toestablish the association in relation to ATS.This is the first systematic review to report the rela-

tionship between ATS and HRF in youth. In addition, theassessment of study eligibility and quality was conductedindependently by two authors and consensus was reachedfor every decision. However, the limitations of this reviewshould be noted. First, the measurement and classifica-tion of ATS were different across studies, which mayexplain some of the inconsistencies found. Second, thequality assessment was not extensive and additional cri-teria may provide additional insights. Finally, the searchstrategy was limited to published studies identifiedthrough the selected search engines. As more studiescontinue to be published, it will be important to reconsi-der and refine these findings. Despite these shortcomings,it seems that the majority of higher quality studiesreported consistent associations between ATS and theimportant health indexes of weight status/body composi-tion and cardiorespiratory fitness. With such health ben-efits for children, the promotion of ATS provides a realopportunity for public health interventions to tackle thedouble challenge of obesity and poor fitness.

ConclusionsThere is some evidence to suggest that active travel toschool is associated with a healthier body compositionand level of cardiorespiratory fitness among youth.However, as this review included only two longitudinalstudies, more interventions and longitudinal studiesexploring the relationship between changes in health-related fitness and active travel are needed to investigatethe causal nature of such relationships. While it isimportant that researchers continue to monitor childrenand adolescents school transportation behaviours, thereis a need to broaden the research on young people’sactive transport to include journeys to destinationsother than school, e.g. walking/cycling trips from hometo a variety of neighbourhood destinations. Distance toschool has been identified as a barrier to active trans-portation [50] and public health strategies promotingactive transportation to more proximal destinations areclearly warranted.


of 12

Author details1School of Education, University of Newcastle, Callaghan Campus, Australia.2Department of Public Health, University of Oxford, UK. 3Institute for Sportand Health, University College Dublin, Ireland.

Authors’ contributionsDRL and CEF conducted the literature search. DRL and PK evaluated thequality of included studies. All authors contributed the editing andapproving of the final version of the paper.

Competing interestsThe authors declare that they have no competing interests.

Received: 3 August 2010 Accepted: 26 January 2011Published: 26 January 2011

References1. Mellin AE, Neumark-Sztainer D, Story M, Ireland M, Resnick MD: Unhealthy

behaviors and psychosocial difficulties among overweight adolescents:The potential impact of familial factors. J Adolesc Health 2002,31(2):145-153.

2. Tremblay MS, Williams JD: Is the Canadian childhood obesity epidemicrelated to physical inactivity? Int J Obes Relat Metab Disord 2003, 27:1100-1105.

3. Baker JL, Olsen LW, Sorensen TIA: Childhood body-mass index and the risk ofcoronary heart disease in adulthood. N Engl J Med 2007, 357(23):2329-2337.

4. Westerterp KR, Speakman JR: Physical activity energy expenditure has notdeclined since the 1980s and matches energy expenditures of wildmammals. Int J Obes 2008, 32:1256-1263.

5. Van der Ploeg HP, Merom D, Corpuz G, Bauman AE: Trends in Australianchildren traveling to school 1971-2003: Burning petrol or carbohydrates?Prev Med 2008, 46(1):60-62.

6. McDonald NC: Active transportation to school: trends among U.S. schoolchildren, 1969-2001. Am J Prev Med 2007, 32(6):509-516.

7. Buliung RN, Mitra R, Faulkner G: Active school transportation in theGreater Toronto Area, Canada: An exploration of trends in space andtime (1986-2006). Prev Med 2009, 48:507-512.

8. Tudor-Locke C, Ainsworth BE, Adair LS, Popkin BM: Objective physicalactivity of Filipino youth stratified for commuting mode to school. MedSci Sports Exerc 2003, 35(3):465-471.

9. Davison KK, Werder JL, Lawson CT: Children’s active commuting to school:current knowledge and future directions. Prev Chronic Dis 2008, 5(3)[http://www.cdc.gov/pcd/].

10. Faulkner GEJ, Buliung RN, Flora PK, Fusco C: Active school transport,physical activity levels and body weight of children and youth: Asystematic review. Prev Med 2009, 48:3-8.

11. Lee MC, Orenstein MR, Richardson MJ: Systematic review of activecommuting to school and children’s physical activity and weight. J PhysAct Health 2008, 5:930-949.

12. Higgins JPT, Green S, (editors): Cochrane Handbook for Systematic Reviews ofInterventions Version 5.0.2 [updated September 2009]: The CochraneCollaboration 2009 [http://www.cochrane-handbook.org].

13. Armstrong N, McManus A: Children’s fitness and physical activity: Achallenge for physical education. Br J Teach Phys Educ 1994, 25(1):20-26.

14. Janssen I: Physical activity guidelines for children and youth. Appl PhysiolNutr Metab 2007, 32:S109-S121.

15. Andersen LB, Lawlor DA, Cooper AR, Froberg K, Anderssen SA: Physicalfitness in relation to school in adolescents: the Danish youth and sportsstudy. Scand J Med Sci Sports 2009, 19:406-411.

16. Moher D, Cook DJ, Eastwood S, Olkin S, Rennie D, Stroup DF: Improvingthe quality of reports of meta-analyses of randomised controlled trials:the QUOROM statement. Lancet 1999, 354(27):1896-1900.

17. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC,Vandenbroucke JP, for the STROBE initiative: The Strengthening theReporting of Observational Studies in Epidemiology (STROBE) statement:guidelines for reporting observational studies. Prev Med 2007,370(9596):1453-1457.

18. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates of physical activityof children and adolescents. Med Sci Sports Exerc 2000, 32(5):963-975.

19. Evenson KR, Huston SL, McMillen BJ, Bors P, Ward DS: Statewideprevalence and correlates of walking and bicycling to school. ArchPediatr Adolesc Med 2003, 157(9):887-892.

20. Gordon-Larsen P, Nelson MC, Beam K: Associations among activetransportation, physical activity, and weight status in young adults. ObesRes 2005, 13(5):868-875.

21. Fulton JE, Shisler JL, Yore MM, Caspersen CJ: Active transportation to school:Findings from a national survey. Res Q Exerc Sport 2005, 76(3):352-357.

22. Sirard JR, Riner WF, Mciver K, Pate RR: Physical activity and activecommuting to elementary school. Med Sci Sports Exerc 2005,37(12):2062-2069.

23. Rosenberg DE, Sallis JF, Conway TL, Cain KL, McKenzie TL: Activetransportation to school over 2 years in relation to weight status andphysical activity. Obes Res 2006, 14(10):1771-1776.

24. Saksvig BI, Catellier DJ, Pfeiffer K, Schmitz KH, Conway TL, Going S, Ward D,Strikmiller P, Treuth M: Travel by walking before and after school andphysical activity among adolescent girls. Arch Pediatr Adolesc Med 2007,161:153-158.

25. Madsen KA, Gosliner W, Woodward-Lopez G, Crawford PB: Physical activityopportunities associated with fitness and weight status amongadolescents in low-Income communities. Arch Pediatr Adolesc Med 2009,163(11):1014-1021.

26. Metcalf B, Voss L, Jeffery A, Perkins J, Wilkin T: Physical activity cost of theschool run: impact on school children of being driven to school. BMJ2004, 329:832-833.

27. Ford P, Bailey R, Coleman D, Woolf-May K, Swaine I: Activity levels, dietaryenergy intake, and body composition in children who walk to school.Pediatr Exerc Sci 2007, 19:393-407.

28. Baig F, Hameed MA, Shorthouse G, Roalfe AK, Daley A: Associationbetween active commuting to school, weight and physical activitystatus in ethnically diverse adolescents predominately living in deprivedcommunities. Pub Health 2009, 123:39-41.

29. Voss C, Sandercock G: Aerobic fitness and mode of travel to school inEnglish school children. Med Sci Sports Exerc 2010, 42(2):281-287.

30. De Bourdeaudhuij I, Leferve J, Deforche B, Wijndaele K, Matton L,Philippaerts R: Physical activity and psychosocial correlates in normalweight and overweight 11 to 19 year olds. Obes Res 2005, 13(6):1097-1105.

31. Klein-Platat C, Oujaa M, Wagner A, Haan MC, Arveiler D, Schlienger JL,Simon C: Physical activity is inversely related to waist circumference in12-y-old French adolescents. Int J Obes 2005, 29:9-14.

32. Cooper AR, Wedderkopp H, Wang LB, Andersen K, Froberg K, Page AS:Active travel to school and cardiovascular fitness in Danish children andadolescents. Med Sci Sports Exerc 2006, 38(10):1724-1731.

33. Cooper AR, Wedderkopp N, Jago R, Kristensen PL, Moller NC, Froberg K,Page AS, Andersen LB: Longitudinal associations of cycling to school withadolescent fitness. Prev Med 2008, 47:324-328.

34. Mota J, Gomes H, Almeida M, Ribeiro JC, Carvalho J, Santos MP: Activeversus passive transportation to school - differences in screen time,socio-economic position and perceived environmental characteristics inadolescent girls. Ann Hum Biol 2007, 34(3):273-282.

35. Mota J, Riberio J, Santos MP, Gomes H: Obesity, physical activity,computer use, and TV viewing in Portuguese adolescents. Pediatr ExercSci 2006, 17:113-121.

36. Timperio A, Ball K, Salmon J, Roberts R, Giles-Corti B, Simmons D, Baur LA,Crawford D: Personal, family, social, and environmental correlates ofactive commuting to school. Am J Prev Med 2006, 30(1):45-51.

37. Yeung J, Wearing S, Hills AP: Child transport practices and perceivedbarriers in active commuting to school. Transp Res Part A 2008,42:895-900.

38. Li Y, Zhai F, Yang X, Schouten EG, Hu X, He Y, Luan D, Ma G: Determinantsof childhood overweight and obesity in China. Br J Nutr 2007, 97:210-215.

39. Ortega FB, Tresaco B, Ruiz JR, Moreno LA, Martin-Matillas M, Mesa JL,Warnberg J, Bueno M, Tercedor P, Gutierrez A, et al: Cardiorespiratoryfitness and sedentary activities are associated with adiposity inadolescents. Obes 2007, 15(6):1589-1599.

40. Landsberg B, Plachta-Danielzik S, Much D, Johannsen M, Lange D,Muller MJ: Associations between active commuting to school, fat massand lifestyle factors in adolescents: the Kiel Obesity Prevention Study(KOPS). Eur J Clin Nutr 2008, 62:739-747.

41. Robertson-Wilson JE, Leatherdale ST, Wong SL: Social-ecological correlatesof active commuting to school among high school students. J AdolescHealth 2008, 42:486-495.

42. Silva KS, Lopes AS: Excess weight, arterial pressure and physical activityin commuting to school: Correlations. Arq Bras Cardiol 2008, 91(2):84-91.


of 12

http://www.ncbi.nlm.nih.gov/pubmed/12127384?dopt=Abstract


















http://www.cdc.gov/pcd/






http://www.cochrane-handbook.org

















































43. Heelan KA, Donnelly JE, Jacobsen DJ, Mayo MS, Washburn R, Greene L:Active commuting to and from school and BMI in elementary schoolchildren - preliminary data. Child Care Health Dev 2005, 31(3):341-349.

44. Chillón P, Ortega FB, Ruiz JR, Veidebaum T, Oja L, Mäestu J, Sjöström M:Active commuting to school in children and adolescents: Anopportunity to increase physical activity and fitness. Scand J Public Health2010, 38(8):873-879.

45. Page AS, Cooper AR, Griew P, Jago R: Independent mobility, perceptionsof the built environment and children’s participation in play, activetravel and structured exercise and sport: the PEACH Project. Int J BehavNutr Phys Act 2010, 7(17).

46. Jones AP, Coombes EG, Griffin SJ, van Sluijs EM: Environmentalsupportiveness for physical activity in English schoolchildren: a studyusing Global Positioning Systems. Int J Behav Nutr Phys Act 2009, 6(42).

47. Kelly P, Foster C: A new technology for measuring our journeys: resultsfrom a pilot study. International Society for the Study of Behavioral Nutritionand Physical Activity 2010 Annual Meeting: June 9-12, 2010: 2010 Minneapolis,Minnesota, USA; 2010.

48. Panter JR, Jones AP, van Sluijs EM: Environmental determinants of activetravel in youth: A review and framework for future research. Int J BehavNutr Phys Act 2008, 5(34).

49. Foster C, Cowburn G, Allender S: Interventions for promoting physicalactivity to children and young people: Review of qualitative studies.London: NICE; 2008.

50. Wen LM, Fry D, Rissel C, Dirkis H, Balafas A, Merom D: Factors associatedwith children being driven to school: implications for walk to schoolprograms. Health Educ Res 2008, 23(2):325-334.

doi:10.1186/1479-5868-8-5Cite this article as: Lubans et al.: The relationship between active travelto school and health-related fitness in children and adolescents: asystematic review. International Journal of Behavioral Nutrition and PhysicalActivity 2011 8:5.

Submit your next manuscript to BioMed Centraland take full advantage of:

• Convenient online submission

• Thorough peer review

• No space constraints or color figure charges

• Immediate publication on acceptance

• Inclusion in PubMed, CAS, Scopus and Google Scholar

• Research which is freely available for redistribution

Submit your manuscript at www.biomedcentral.com/submit


of 12













The relationship between active travel to school and health

Documents

Transcript of The relationship between active travel to school and health